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	Graph Representation Learning-Guided Diffusion Model for Hyperspectral Change Detection 期刊论文 OAI收割 IEEE Geoscience and Remote Sensing Letters, 2024, 卷号: 21, 页码: 1-5 作者: Ding, Xinyu; Qu, Jiahui; Dong, Wenqian; Zhang, Tongzhen; Li, Nan \| 收藏 \| 浏览/下载：0/0 \| 提交时间：2024/09/24 Change detection difference perception amplification diffusion model graph convolutional network (GCN) hyperspectral images (HSIs)
	Cognition-Driven Multiagent Policy Learning Framework for Promoting Cooperation 期刊论文 OAI收割 IEEE TRANSACTIONS ON GAMES, 2023, 卷号: 15, 期号: 3, 页码: 388-398 作者: \| 收藏 \| 浏览/下载：15/0 \| 提交时间：2023/11/16 Cognition difference coupling cognition network (CCN) deep reinforcement learning (DRL) graph convolutional network multiagent systems (MASs)
	Nonlocal feature learning based on a variational graph auto-encoder network for small area change detection using SAR imagery 期刊论文 OAI收割 ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING, 2022, 卷号: 193, 页码: 137-149 作者: Su, Hang; Zhang, Xinzheng; Luo, Yuqing; Zhang, Ce; Zhou, Xichuan \| 收藏 \| 浏览/下载：45/0 \| 提交时间：2022/11/29 Synthetic aperture radar Change detection Difference image Graph auto -encoder network Deep learning
	Object-difference drived graph convolutional networks for visual question answering 期刊论文 OAI收割 MULTIMEDIA TOOLS AND APPLICATIONS, 2020, 页码: 19 作者: Zhu, Xi; Mao, Zhendong; Chen, Zhineng; Li, Yangyang; Wang, Zhaohui \| 收藏 \| 浏览/下载：37/0 \| 提交时间：2020/06/02 Visual question answering Graph convolutional networks Object-difference
	Tool orientation optimization for 5-axis machining with C-space method 期刊论文 OAI收割 INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY, 2017, 卷号: 88, 期号: 5-8, 页码: 1243-1255 作者: Mi, Zhenpeng; Yuan, Chun-Ming; Ma, Xiaohui; Shen, Li-Yong \| 收藏 \| 浏览/下载：22/0 \| 提交时间：2018/07/30 Five-axis machining C-space Dijkstra's algorithm Difference graph Tool orientation
	基于二进制补丁比对的软件输入数据自动构造期刊论文 OAI收割计算机工程与设计, 2010, 卷号: 31, 期号: 14, 页码: 3169-3173 沈亚楠; 赵荣彩; 任华; 王小芹; 刘振华; 张新宇; 李鹏飞 \| 收藏 \| 浏览/下载：24/0 \| 提交时间：2011/05/23 软件二进制补丁比对函数调用图遗传算法适应值函数软件输入数据自动构造software binary path difference function call graph evolutionary algorithm fitness function automatic software input data construct
	Graph fitting test method for the interpolation error of Moire fringe (EI CONFERENCE) 会议论文 OAI收割 9th International Conference on Electronic Measurement and Instruments, ICEMI 2009, August 16, 2009 - August 19, 2009, Beijing, China 作者: Sun Y. 收藏 \| 浏览/下载：30/0 \| 提交时间：2013/03/25 In order to realize the fast test for the interpolation error the graph fitting test method for the interpolation error of Moire fringe is put forward in this paper. Firstly the triangular wave Moire fringe photoelectric signal of the encoder whose phase difference is 90 are sampled to get the Lissajous graph of the two signals. Secondly the single wave represented by the founded subsection function is used to fit the practical Moire fringe Lissajous graph. Then the fitting result is tested to verify whether it satisfies the accuracy requirement. Lastly the founded subsection function instead of the practical wave function is used to calculate the interpolation error. Using the graph fitting method to sample the Moire fringe single of 15-bits photoelectric encoder to get the interpolation error curve the tested maximum interpolation error is 70" and the minimum error is - 69". Comparing with the interpolation error which is received from traditional test method the change trend of the interpolation error curve is similar and peak-peak value is almost equality. The results of experiment indicate that: the equipment is convenient and the examination method is efficient and feasible. The measure speed is fast and the manifestation result is intuitionistic. The system can be used in the working field. The method can avoid the speed influence and realize the dynamic interpolation error measure which is significant for the research of encoder's dynamic accuracy characteristics. 2009 IEEE.